Model Monetary Data

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Overview

Numeric Model
Non-Numeric Model

Overview

Applications that handle monetary data often require the ability to capture fractional units of currency and need to emulate decimal rounding with exact precision when performing arithmetic. The binary-based floating-point arithmetic used by many modern systems (i.e., float, double) is unable to represent exact decimal fractions and requires some degree of approximation making it unsuitable for monetary arithmetic. This constraint is an important consideration when modeling monetary data.

There are several approaches to modeling monetary data in MongoDB using the numeric and non-numeric models.

Numeric Model

The numeric model may be appropriate if you need to query the database for exact, mathematically valid matches or need to perform server-side arithmetic, e.g., $inc, $mul, and aggregation pipeline arithmetic.

The following approaches follow the numeric model:

Using the Decimal BSON Type which is a decimal-based floating-point format capable of providing exact precision.
Using a Scale Factor to convert the monetary value to a 64-bit integer (long BSON type) by multiplying by a power of 10 scale factor.

Non-Numeric Model

If there is no need to perform server-side arithmetic on monetary data or if server-side approximations are sufficient, modeling monetary data using the non-numeric model may be suitable.

The following approach follows the non-numeric model:

Using two fields for the monetary value: One field stores the exact monetary value as a non-numeric string and another field stores a binary-based floating-point (double BSON type) approximation of the value.

Note

Arithmetic mentioned on this page refers to server-side arithmetic performed by mongod or mongos, and not to client-side arithmetic.

Numeric Model

Using the Decimal BSON Type

The decimal128 BSON type uses the IEEE 754 decimal128 decimal-based floating-point numbering format. Unlike binary-based floating-point formats such as the double BSON type, decimal128 does not approximate decimal values and is able to provide the exact precision required for working with monetary data.

In mongosh, decimal values are assigned and queried using the Decimal128() constructor. The following example adds a document containing gas prices to a gasprices collection:

db.gasprices.insertOne(
   {
      "date" : ISODate(),
      "price" : Decimal128("2.099"),
      "station" : "Quikstop",
      "grade" : "regular"
   }
)

The following query matches the document above:

db.gasprices.find( { price: Decimal128("2.099") } )

For more information on the decimal type, see Decimal128.

Converting Values to Decimal

A collection's values can be transformed to the decimal type by performing a one-time transformation or by modifying application logic to perform the transformation as it accesses records.

Tip

Alternative to the procedure outlined below, starting in version 4.0, you can use the $convert and its helper $toDecimal operator to convert values to Decimal128().

One-Time Collection Transformation

A collection can be transformed by iterating over all documents in the collection, converting the monetary value to the decimal type, and writing the document back to the collection.

Note

It is strongly advised to add the decimal value to the document as a new field and remove the old field later once the new field's values have been verified.

Warning

Be sure to test decimal conversions in an isolated test environment. Once datafiles are created or modified they will no longer be compatible with previous versions and there is no support for downgrading datafiles containing decimals.

Scale Factor Transformation:

Consider the following collection which used the Scale Factor approach and saved the monetary value as a 64-bit integer representing the number of cents:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : NumberLong("1999") },
{ "_id" : 2, "description" : "Jeans", "size" : "36", "price" : NumberLong("3999") },
{ "_id" : 3, "description" : "Shorts", "size" : "32", "price" : NumberLong("2999") },
{ "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : NumberLong("2495") },
{ "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : NumberLong("8000") }

The long value can be converted to an appropriately formatted decimal value by multiplying price and NumberDecimal("0.01") using the $multiply operator. The following aggregation pipeline assigns the converted value to the new priceDec field in the $addFields stage:

db.clothes.aggregate(
  [
    { $match: { price: { $type: "long" }, priceDec: { $exists: 0 } } },
    {
      $addFields: {
        priceDec: {
          $multiply: [ "$price", NumberDecimal( "0.01" ) ]
        }
      }
    }
  ]
).forEach( ( function( doc ) {
  db.clothes.replaceOne( doc );
} ) )

The results of the aggregation pipeline can be verified using the db.clothes.find() query:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : NumberLong(1999), "priceDec" : NumberDecimal("19.99") }
{ "_id" : 2, "description" : "Jeans", "size" : "36", "price" : NumberLong(3999), "priceDec" : NumberDecimal("39.99") }
{ "_id" : 3, "description" : "Shorts", "size" : "32", "price" : NumberLong(2999), "priceDec" : NumberDecimal("29.99") }
{ "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : NumberLong(2495), "priceDec" : NumberDecimal("24.95") }
{ "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : NumberLong(8000), "priceDec" : NumberDecimal("80.00") }

If you do not want to add a new field with the decimal value, the original field can be overwritten. The following updateMany() method first checks that price exists and that it is a long, then transforms the long value to decimal and stores it in the price field:

db.clothes.updateMany(
  { price: { $type: "long" } },
  { $mul: { price: NumberDecimal( "0.01" ) } }
)

The results can be verified using the db.clothes.find() query:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : NumberDecimal("19.99") }
{ "_id" : 2, "description" : "Jeans", "size" : "36", "price" : NumberDecimal("39.99") }
{ "_id" : 3, "description" : "Shorts", "size" : "32", "price" : NumberDecimal("29.99") }
{ "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : NumberDecimal("24.95") }
{ "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : NumberDecimal("80.00") }

Non-Numeric Transformation:

Consider the following collection which used the non-numeric model and saved the monetary value as a string with the exact representation of the value:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : "19.99" }
{ "_id" : 2, "description" : "Jeans", "size" : "36", "price" : "39.99" }
{ "_id" : 3, "description" : "Shorts", "size" : "32", "price" : "29.99" }
{ "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : "24.95" }
{ "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : "80.00" }

The following function first checks that price exists and that it is a string, then transforms the string value to a decimal value and stores it in the priceDec field:

db.clothes.find( { $and : [ { price: { $exists: true } }, { price: { $type: "string" } } ] } ).forEach( function( doc ) {
  doc.priceDec = NumberDecimal( doc.price );
  db.clothes.replaceOne( doc );
} );

The function does not output anything to the command line. The results can be verified using the db.clothes.find() query:

{ "_id" : 1, "description" : "T-Shirt", "size" : "M", "price" : "19.99", "priceDec" : NumberDecimal("19.99") }
{ "_id" : 2, "description" : "Jeans", "size" : "36", "price" : "39.99", "priceDec" : NumberDecimal("39.99") }
{ "_id" : 3, "description" : "Shorts", "size" : "32", "price" : "29.99", "priceDec" : NumberDecimal("29.99") }
{ "_id" : 4, "description" : "Cool T-Shirt", "size" : "L", "price" : "24.95", "priceDec" : NumberDecimal("24.95") }
{ "_id" : 5, "description" : "Designer Jeans", "size" : "30", "price" : "80.00", "priceDec" : NumberDecimal("80.00") }

Application Logic Transformation

It is possible to perform the transformation to the decimal type from within the application logic. In this scenario the application modified to perform the transformation as it accesses records.

The typical application logic is as follows:

Test that the new field exists and that it is of decimal type
If the new decimal field does not exist:
- Create it by properly converting old field values
- Remove the old field
- Persist the transformed record

Using a Scale Factor

Note

Using the decimal type for modeling monetary data is preferred over the Scale Factor method.

To model monetary data using the scale factor approach:

Determine the maximum precision needed for the monetary value. For example, your application may require precision down to the tenth of one cent for monetary values in USD currency.
Convert the monetary value into an integer by multiplying the value by a power of 10 that ensures the maximum precision needed becomes the least significant digit of the integer. For example, if the required maximum precision is the tenth of one cent, multiply the monetary value by 1000.
Store the converted monetary value.

For example, the following scales 9.99 USD by 1000 to preserve precision up to one tenth of a cent.

{ price: 9990, currency: "USD" }

The model assumes that for a given currency value:

The scale factor is consistent for a currency; i.e. same scaling factor for a given currency.
The scale factor is a constant and known property of the currency; i.e applications can determine the scale factor from the currency.

When using this model, applications must be consistent in performing the appropriate scaling of the values.

For use cases of this model, see Numeric Model.

Non-Numeric Model

To model monetary data using the non-numeric model, store the value in two fields:

In one field, encode the exact monetary value as a non-numeric data type; e.g., BinData or a string.
In the second field, store a double-precision floating point approximation of the exact value.

The following example uses the non-numeric model to store 9.99 USD for the price and 0.25 USD for the fee:

{
  price: { display: "9.99", approx: 9.9900000000000002, currency: "USD" },
  fee: { display: "0.25", approx: 0.2499999999999999, currency: "USD" }
}

With some care, applications can perform range and sort queries on the field with the numeric approximation. However, the use of the approximation field for the query and sort operations requires that applications perform client-side post-processing to decode the non-numeric representation of the exact value and then filter out the returned documents based on the exact monetary value.

For use cases of this model, see Non-Numeric Model.

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